COVID-19 in Children With Rheumatic Diseases in the Spanish National Cohort EPICO-AEP
Bibliographic record
Abstract
To the Editor: SARS-CoV-2 infection in children is relatively mild. Approximately 10% of identified cases are pediatric,1 with a small proportion needing hospitalization. About 25–60% of children admitted with the coronavirus disease 2019 (COVID-19) have comorbidities.2,3 Studies in adults with rheumatic diseases (RD) show that immune-mediated inflammatory disease and use of biologics are not associated with a worse clinical outcome of COVID-19.4,5,6,7 However, if patients have poorly controlled active RD or receive corticosteroids, they may be at an increased risk of infection and serious disease. Based on previous case series, scientific associations have released management recommendations for these patients.8,9 We aimed to describe the prevalence of RD among children younger than 18 years with SARS-CoV-2 infection at the 49 hospitals included in the Spanish national cohort EPICO-AEP. This study was approved by the Ethics Committee of the University Hospital 12 de Octubre (code 20/101). Informed consent was obtained from parents and mature minors. By June 30, 2020, there were 350 children admitted to the hospital, of which 48 (13.7%) required intensive care unit admission, and 4 (1.1%) died. Among the pediatric patients admitted with COVID-19, 8 … Address correspondence to Dr. C. Calvo, Hospital Universitario La Paz, Pº Castellana, 261.28046 Madrid, Spain. Email: ccalvorey{at}gmail.com.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".